RESEARCH ARTICLE
Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness Yue Gao1*, Shimon Edelman2 1 Dept. of Computer Science, Cornell University, Ithaca, NY, United States of America, 2 Dept. of Psychology, Cornell University, Ithaca, NY, United States of America *
[email protected]
Abstract a11111
OPEN ACCESS Citation: Gao Y, Edelman S (2016) Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness. PLoS ONE 11(5): e0153193. doi:10.1371/journal.pone.0153193
We offer and test a simple operationalization of hedonic and eudaimonic well-being (“happiness”) as mediating variables that link outcomes to motivation. In six evolutionary agentbased simulation experiments, we compared the relative performance of agents endowed with different combinations of happiness-related traits (parameter values), under four types of environmental conditions. We found (i) that the effects of attaching more weight to longerterm than to momentary happiness and of extending the memory for past happiness are both stronger in an environment where food is scarce; (ii) that in such an environment “relative consumption,” in which the agent’s well-being is negatively affected by that of its neighbors, is more detrimental to survival when food is scarce; and (iii) that having a positive outlook, under which agents’ longer-term happiness is increased by positive events more than it is decreased by negative ones, is generally advantageous.
Editor: Wen-Bo Du, Beihang University, CHINA Received: December 8, 2015 Accepted: March 24, 2016 Published: May 4, 2016 Copyright: © 2016 Gao, Edelman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data and code are available at https://www.openabm.org/ model/4934. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist.
Introduction Happiness and other emotional states play a central role in human existence by mediating the regulation of behavior [1–4]. While this construal of emotions was originally motivated by classical control theory [5], it fits well within the emerging integrative computational framework for understanding the brain/mind, which holds that minds are bundles of computational processes implemented by embodied and physically and socially situated brains [6]. The computational framework allows one to put forward and test very explicit functional models of emotions. In this paper, we use such a model to investigate, in an evolutionary setting, a series of questions pertaining to happiness. These include (i) the role of balancing momentary well-being against longer-term contentment (the “happiness of pursuit” [7]); (ii) the effects of drawing a contrast between oneself and one’s social circle (what economists term “relative consumption” [8, 9]; cf. the concept of social comparison [10, 11]); and (iii) the adaptive role of differential sensitivity to positive and negative turns in momentary well-being. Happiness and other emotions are experienced subjectively; indeed, it is the subjective wellbeing (SWB) that psychologists who study happiness require that the participants in their
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Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness
experiments report [12]. To study happiness in simple computational models that are obviously devoid of any phenomenality or subjectivity [13, 14], we need an objective “handle” onto emotions, which would make explicit their role in behavior and evolution. For this purpose, it suffices to limit our consideration to the valuation aspects of emotions [12, 15]. Our theoretical approach is therefore based on the following set of interlinked premises: 1. Subjectivity, including phenomenal awareness, evolves to serve as an effective tool for connecting action outcomes to motivation [16, 17]. 2. Subjective valuation and the affective states or emotions that mediate it, including happiness, serve as a key pressure point through which evolution acts on the mind [7, 18, 19]. 3. Emotions are information channels whereby phenomenal states and valuation processes motivate decisions, regulate behavior [15, 20], and fine-tune learning [21]. As such, emotions pervade all of cognition [3, 20, 22, 23]. 4. There exist heritable individual differences in the contributions of cognitive, motivational, and affective states and processes to well-being [24, 25]. 5. Happiness-related traits affect the evolutionary fitness of their carriers [26], in ways that depend on physical and social circumstances. In the remainder of this paper, we report the results of six experiments in which we explored the evolutionary dynamics of happiness as a mediator between action outcomes and life evaluation on the one hand and action selection on the other hand. We begin with a brief review of related work and of the literature that supports our working assumptions.
Related work and the present approach In this section, we briefly discuss (i) the role of emotions and motivation in driving behavior; (ii) agent-based evolutionary simulation as a tool for studying these topics; (iii) the hedonic and eudaimonic components of happiness and their further factorization; (iv) social context as a key factor; (v) the role of variables that control the temporal dynamics of hedonic and eudaimonic well-being.
Emotion, motivation, action Behavior is considered to be motivated if it is at least partly determined by its expected consequences [2]. Because the consequences of a planned or future action are not available prior to its execution, the control of motivated behavior involves internal states, which represent goals or expected outcomes. For evolutionary reasons briefly mentioned above, some such states come to be experienced as emotional. Specifically, emotional states, including happiness, convey valuational information, about which the agent by definition cares, and which therefore serves as an effective motivational mechanism for action selection [2, 27]. Motivation is influenced by many types of emotions in addition to happiness. Our goal in the present paper is not to compare the effects of different emotions or to model them; rather, we are interested specifically in the effects of hedonic and eudaimonic well-being.
Agent-based evolutionary simulation modeling While in principle it is possible to study the complex interplay of emotions, motivation, and evolution analytically (e.g., [28]), it is often more practical to do so by resorting to simulation, using an evolutionary agent-based modeling (ABM) approach [29–31]. In ABM, simulated actors (agents) carrying various traits of interest share an environment in which they undertake
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Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness
actions and compete for resources; the agent’s cumulative outcomes determine its fitness, which in turn affects its chances for reproduction. The effectiveness of traits can then be assessed by tracking their prevalence in the population over evolutionary time. The ABM approach has been previously applied to the study of emotions and motivation (e.g., [32–34]). For instance, Malfaz and Salichs [32] used it to model motivation as a combination of internal drives and external stimuli. The drive variables were energy, thirst, health, sociability, and fear; the external stimuli were water, food, and the presence of other agents. In an ABM setting, the relative contributions of the various factors that jointly affect behavior can be tuned using any of a number of learning approaches, in particular reinforcement learning (RL) [35–37], as it was done in [32]. Because our goal in the present work was to determine the effects of specific combinations of the values of relevant parameters, we chose not to allow our agents to learn; a companion paper (Gao and Edelman, in preparation) will report results from a learning-enabled version of our model.
The factorization of happiness A distinction is commonly drawn between two major components of happiness, operationalized as subjective well-being: the hedonic component, estimated through responses to questions such as “How happy are you right now?”, and the eudaimonic component, based on responses to questions such as “How happy are you with your life in general?” [12, 38]. To be useful in an ABM setting, each of these components must be given an explicit mathematical definition in terms of the independent variables of the model. The precise form of such a definition can itself be the subject of an investigation. For instance, Rutledge et al. [39] considered various ways of quantifying subjects’ momentary hedonic SWB H in response to outcomes in an economic game and showed that it is best modeled by combining current task earnings (CR), recent reward expectations (EV), and reward prediction errors (RPE), as follows: H ¼ w0 þ w1
t t t X X X gtj CRj þ w2 gtj EVj þ w3 gtj RPEj t¼1
j¼1
ð1Þ
j¼1
where t is the number of days in memory and 0 γ 1 is a forgetting factor that makes days in more recent trials more influential than those in earlier trials. In the present project, we likewise assume that happiness is related to a time average of outcomes (excluding the RL factors, as noted above) and explore the evolutionary dynamics of traits that control the contributions of momentary and time-averaged values.
The social dimension of hedonic well-being In behavioral economics, it is well known that people’s perceived conditions with regard to the so-called positional goods depend on those of their social circle or comparison group [8, 9]. Intuitively, subjects can be more or less happy with the same absolute level of a positional good (say, a house of a given size), depending on the levels of their neighbors. The study of Baggio and Papyrakis [33] focused on the effects of this type of social comparison. Specifically, they assumed that the hedonic SWB H of agent k in a given year t depended positively on their own income that year, Ykt , and negatively on the average social income for the same period, Y t, as well as its own income in the previous year, Ykt1 : Hkt ¼ Ykt bYkt1 aY t
ð2Þ
where α and β are sensitivity parameters in the range of [0, 1]. They then examined the
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Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness
dependence of H on individual income and social comparison (economic inequality) by considering the effects of the sensitivity parameters in an ABM setting.
The dynamic relationships between hedonic and eudaimonic well-being Intuitively, the eudaimonic SWB, which we shall refer to as E, is expected to be related to the integral of the hedonic SWB, H, but the details of this relationship are up to the modeler. Taking inspiration from Strogatz’s model of love [40], Sprott [41, 42] offered the following secondorder linear differential equation for cumulative happiness, which he denoted by R: d2 R dR þ b þ R ¼ FðtÞ dt 2 dt
ð3Þ
where F(t) is a time-dependent function that quantifies the effects of external events. This equation can be re-written in terms of the momentary well-being, which we call H, in the following Z form: dH þ bH þ H ¼ FðtÞ. Note that this formulation makes explicit the dependence of dt happiness on outcomes, as well as on its history and changes (the integral and derivative terms, respectively).
Methods We now turn to the description of our own work, which uses evolutionary agent-based simulation and draws on some of the ideas mentioned above. In this section, we state the details of our model (see Algorithm 1 for a pseudocode formulation and the S1 Appendix for the ODD protocol [43, 44]). Algorithm 1. Evolutionary simulation of the dynamics of well-being. 1: for generation g = 1 to numGenerations do 2: for actionTime t = 1 to numDays do 3: 4:
Compute motivation Mk
5:
if Mk > θk then
6: 7: 8: 9:
▷ [LIFE CYCLE]
for agent k = 1 to numAgents do ▷ Eq 4 ▷ θ is the threshold for action; Eq 5
k chooses and executes aggressive action else k chooses and executes conservative action end if ▷ food / no food
10:
Determine the outcome
11:
Update the food reward component, f H tk
12:
Update the social component, s H
13:
Update E tk
14:
t k
▷ Eq 7 ▷ Eq 8 ▷ Eq 9
end for
15: end for 16: for agent k = 1 to numAgents do 17: 18:
if fitness F k is in the top 50% then
▷ [END OF LIFE / PROPAGATION] ▷ Eq 10
k produces offspring with the same traits
19:
end if
20:
k terminates
21: end for 22: end for doi:10.1371/journal.pone.0153193.t001
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Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness
Fig 1. An agent’s basic action loop. Motivation prompts actions, which lead to outcomes. Outcomes reap external hedonic rewards (food-related f H, and social s H) and affect reproductive fitness. Hedonic states influence motivation, both directly, with the weight c, and through longer-term (“eudaimonic”) wellbeing E, via the weight 1 − c. The parameters Δt and λ control, respectively, the time window over which E is estimated and the relative contributions of positive and negative changes of H (see Eq 9). After a set number of action cycles, each agent in the top half of the fitness distribution is allowed to produce offspring, which form the next generation; agents that belong to the current generation are terminated. doi:10.1371/journal.pone.0153193.g001
The individual agent: motivation, action, rewards, and well-being We simulate a population of foraging agents, each of which operates according to the actionoutcome-valuation cycle illustrated in Fig 1 (for a more elaborate conception of what the block diagram of an autonomous agent could look like, see [45]). The motivation M of agent k is a weighted sum of its hedonic and eudaimonic well-being, H and E, with the parameter 0 c 1 controlling their relative direct contributions; note that H also contributes to M indirectly, via its effect on E (see below): Mk ¼ c Hk þ ð1 cÞ Ek
ð4Þ
The effect of motivation is controlled by a threshold θ, which depends on the agent’s past motivation: 1 ytk ¼ Pt1 j¼1
ðgj Þ
t1 X ðgti Mk Þ
ð5Þ
i¼1
where the threshold for agent k at time t is a weighted sum of past motivation values, γ 2 [0, 1] being the forgetting factor, which gives more weight to recent motivations. When an agent’s motivation exceeds the threshold θ, it chooses a more aggressive action, by venturing farther away from its present location, in a random direction. If the motivation is below the threshold, the exploration range is shorter. After carrying out the chosen action, the agent updates H, which consists of two components: f H, based on finding food during exploration, and s H, based on social comparison: Hk ¼ ð1 sk Þf H k þ sk s H k
ð6Þ
where sk, agent k’s sociality/food weighting parameter, is in the range of [0, 1]. The food-based component is computed as follows: f
H tk ¼f H t1 þ ak F tk bf k
ð7Þ
where F is the number of food units that the action yielded and αk controls the contribution of
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Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness
food to agent k’s hedonic well-being; βf is the food equivalent of the agents’ energy consumption per action cycle. For each agent, there is an upper bound on the maximum amount of food an agent can gain per cycle. The social component of hedonic well-being s H for agent k at time t is computed as shown below: k 1X Hjt þ bs nk j¼1 n
t t s H k ¼ Hk
ð8Þ
where the trait nk represents the size of agent k’s social comparison group. Intuitively, an agent is happier when it is doing better than group average and less happy otherwise. βs is the base hedonic well-being gain from socialization. The agent’s eudaimonic well-being E is then computed from its present value of H, the memory of the past values of H extending over a number of cycles, and the rates of rise and fall of H. Specifically, i i t 1 X dHk dHk þ sn Hki þ sp E tk ¼ Dtk i¼tDt di di k
sp ðxÞ ¼
8 < p lk x :
0
x0 x
